America’s Most In-Demand Jobs: Beyond the Headlines

Registered Nurses dominate job postings nationwide—but the #2 job reveals a stark divide between Software and Trucking states.

A makeover of Lightcast’s job posting rankings, enhanced with BLS employment data and Census population figures. This three-panel dashboard reveals that while RNs are #1 in 49 of 50 states, the #2 job exposes a regional economic divide—Software states average $125k wages versus $51k for Trucking states. Built with R, ggplot2, and patchwork using a highlight-and-gray color strategy.

Original

The original visualization comes from Most Posted US Jobs by State

Original visualization

Makeover

Figure 1: Three-panel dashboard showing America’s most in-demand jobs. The left panel displays “49 out of 50 states have Registered Nurse as #1.” The right panel shows #2 job distribution: Retail Sales (31 states), Trucking (10 states, amber), Physician (6 states), Software (2 states, purple), and RN (1 state). Bottom panel compares Software vs. Trucking states across three metrics: prevalence (2 vs. 10 states), average wage ($125k vs. $51k), and total employment (82,260 vs. 346,870 workers). Purple and amber highlight the Software-Trucking divide; other categories are shown in gray.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
  tidyverse, ggtext, showtext, scales, glue, patchwork, janitor
)
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 14,
    height = 10,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|

### |- Current data (2025) ----
state_summary  <- read_csv(
   here::here("data/MakeoverMonday/2026/mm_wk04_state_summary.csv")) 

job_summary  <- read_csv(
   here::here("data/MakeoverMonday/2026/mm_wk04_job_summary.csv")) 
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(state_summary)
glimpse(job_summary)
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

# Note: Primary data wrangling was performed in data preparation scripts:
#   - mm_wk04_bls_data_pull.R (BLS OEWS extraction)
#   - mm_wk04_data_merge.R (merge all sources)
# 
# This script reads the processed summary files and performs
# visualization-specific transformations only.

### |- P2 data: #2 job distribution ----
p2_data <- state_summary |>
  count(second_job, name = "n_states") |>
  mutate(
    second_job = fct_reorder(second_job, n_states),
    label = ifelse(n_states == 1, "1 state", paste0(n_states, " states")),
    is_hero = second_job %in% c(
      "Software Developer / Engineer",
      "Tractor-Trailer Truck Driver"
    )
  )

### |- P3 data: Software vs Trucking comparison ----
p3_data <- state_summary |>
  filter(second_job %in% c(
    "Tractor-Trailer Truck Driver",
    "Software Developer / Engineer"
  )) |>
  group_by(second_job) |>
  summarise(
    n_states = n(),
    total_emp = sum(second_job_emp, na.rm = TRUE),
    avg_wage = mean(second_job_wage, na.rm = TRUE),
    .groups = "drop"
  ) |>
  mutate(
    job_short = case_when(
      str_detect(second_job, "Truck") ~ "Trucking",
      str_detect(second_job, "Software") ~ "Software"
    ),
    job_short = factor(job_short, levels = c("Software", "Trucking"))
  )
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    software      = "#7B5E9A",
    trucking      = "#C4915E",
    neutral       = "#A0A4A8",
    text_headline = "#2C3E50",
    text_dark     = "#2C3E50",
    text_mid      = "#5D6D7E",
    text_light    = "#95A5A6"
  )
)

### |-  job color mapping ----
job_colors <- c(
  "Software Developer / Engineer"   = colors$palette$software,
  "Tractor-Trailer Truck Driver"    = colors$palette$trucking,
  "Retail Sales Associate"          = colors$palette$neutral,
  "Physician"                       = colors$palette$neutral,
  "Registered Nurse"                = colors$palette$neutral
)

### |-  Main titles ----
title_text <- "America's Most In-Demand Jobs: Beyond the Headlines"

subtitle_text <- str_glue(
  "Registered Nurses dominate job postings nationwide—but the #2 job reveals a stark divide between Software and Trucking states"
)

caption_text <- create_mm_caption(
  mm_year = 2026, mm_week = 04,
  source_text = str_glue(
    "Lightcast Job Posting Analytics (Oct 2024-2025) | BLS OEWS May 2024 | Census Bureau 2023"
  )
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # # Text styling
    plot.title = element_text(
      size = rel(1.3), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_text(
      size = rel(0.8), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.justification = "right",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),

    # Axis formatting
    # axis.line.x = element_line(color = "#252525", linewidth = .1),
    # axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.text.y = element_markdown(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),

    # Grid lines
    panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |-  P1 - THE BIG NUMBER ---- 
p1 <- ggplot() +
  # Annotate
  annotate(
    "text",
    x = 0.5, y = 0.75,
    label = "49",
    size = 40,
    fontface = "bold",
    color = colors$palette$text_headline
  ) +
  annotate(
    "text",
    x = 0.5, y = 0.32,
    label = "out of 50 states have\nRegistered Nurse\nas the #1 most-posted job",
    size = 4.5,
    color = colors$palette$text_dark,
    lineheight = 1.2
  ) +
  annotate(
    "text",
    x = 0.5, y = 0.08,
    label = "The one exception? One state has Retail Sales Associate in the top spot.",
    size = 3.5,
    color = colors$palette$text_mid,
    fontface = "italic"
  ) +
  # Limits
  xlim(0, 1) +
  ylim(0, 1) +
  # Theme
  theme_void() +
  theme(
    plot.background = element_rect(fill = colors$background, color = colors$background),
    panel.background = element_rect(fill = colors$background, color = colors$background),
    plot.margin = margin(10, 10, 10, 10)
  )

### |-  P2 - #2 JOB DISTRIBUTION ----
p2 <- p2_data |>
  ggplot(aes(x = n_states, y = second_job, fill = second_job)) +
  # Geoms
  geom_col(width = 0.7) +
  geom_text(
    aes(
      label = label,
      fontface = ifelse(is_hero, "bold", "plain")
    ),
    hjust = -0.1,
    size = 4,
    color = colors$palette$text_dark
  ) +
  # Scales
  scale_fill_manual(values = job_colors, guide = "none") +
  scale_x_continuous(expand = expansion(mult = c(0, 0.25))) +
  # Labs
  labs(
    title = "What's Your State's #2 Most-Posted Job?",
    subtitle = "Since RN dominates #1, the real variation is in the second spot",
    x = NULL,
    y = NULL
  ) +
  # Theme
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.x = element_blank(),
    plot.margin = margin(10, 10, 10, 10)
  )

### |-  P3 - THE OPPORTUNITY GAP ---

# Sub-panel A: Number of states
p3a <- p3_data |>
  ggplot(aes(x = job_short, y = n_states, fill = second_job)) +
  # Geoms
  geom_col(width = 0.6) +
  geom_text(
    aes(label = paste0(n_states, " states")),
    vjust = -0.5,
    fontface = "bold",
    size = 3.5,
    color = colors$palette$text_dark
  ) +
  # Scales
  scale_fill_manual(values = job_colors, guide = "none") +
  scale_y_continuous(expand = expansion(mult = c(0, 0.25))) +
  # Labs
  labs(title = "Where It's #2") +
  # Theme
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.title.y = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_blank(),
    plot.margin = margin(10, 10, 10, 10)
  )

# Sub-panel B: Average wage
p3b <- p3_data |>
  ggplot(aes(x = job_short, y = avg_wage, fill = second_job)) +
  # Geoms
  geom_col(width = 0.6) +
  geom_text(
    aes(label = scales::dollar(avg_wage, accuracy = 1, scale = 0.001, suffix = "k")),
    vjust = -0.5,
    fontface = "bold",
    size = 3.5,
    color = colors$palette$text_dark
  ) +
  # Scales
  scale_fill_manual(values = job_colors, guide = "none") +
  scale_y_continuous(expand = expansion(mult = c(0, 0.25))) +
  # Labs
  labs(title = "Average Wage") +
  # Theme
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.title.y = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_blank(),
    plot.margin = margin(10, 10, 10, 10)
  )

# Sub-panel C: Total employment
p3c <- p3_data |>
  ggplot(aes(x = job_short, y = total_emp, fill = second_job)) +
  # Geoms
  geom_col(width = 0.6) +
  geom_text(
    aes(label = scales::comma(total_emp)),
    vjust = -0.5,
    fontface = "bold",
    size = 3.5,
    color = colors$palette$text_dark
  ) +
  # Scales
  scale_fill_manual(values = job_colors, guide = "none") +
  scale_y_continuous(expand = expansion(mult = c(0, 0.25))) +
  # Labs
  labs(title = "Total Employment") +
  # Theme
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.title.y = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_blank(),
    plot.margin = margin(10, 10, 10, 10)
  )

# Combine P3
p3 <- (p3a | p3b | p3c)

### |-  combined plot ----
combined_plot <- (p1 | p2) / p3 +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(2.2),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.15,
        margin = margin(t = 5, b = 10)
      ),
      plot.subtitle = element_text(
        size = rel(0.9),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.88),
        lineheight = 1.5,
        margin = margin(t = 5, b = 10)
      ),
      plot.caption = element_markdown(
        size = rel(0.65),
        family = fonts$subtitle,
        color = colors$caption,
        hjust = 0,
        lineheight = 1.4,
        margin = margin(t = 20, b = 5)
      ),
    )
  )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plot, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 14, 
  height = 10
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      janitor_2.2.0   patchwork_1.3.0 glue_1.8.0     
 [5] scales_1.3.0    showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         parallel_4.4.0     gifski_1.32.0-1    fansi_1.0.6       
[13] pkgconfig_2.0.3    ggplotify_0.1.2    lifecycle_1.0.4    compiler_4.4.0    
[17] farver_2.1.2       munsell_0.5.1      codetools_0.2-20   snakecase_0.11.1  
[21] htmltools_0.5.8.1  yaml_2.3.10        crayon_1.5.3       pillar_1.9.0      
[25] camcorder_0.1.0    magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1  
[29] digest_0.6.37      stringi_1.8.4      labeling_0.4.3     rsvg_2.6.1        
[33] rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1  
[37] cli_3.6.4          magrittr_2.0.3     utf8_1.2.4         withr_3.0.2       
[41] bit64_4.5.2        timechange_0.3.0   rmarkdown_2.29     bit_4.5.0         
[45] hms_1.1.3          evaluate_1.0.1     knitr_1.49         markdown_1.13     
[49] gridGraphics_0.5-1 rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1     
[53] xml2_1.3.6         renv_1.0.3         svglite_2.1.3      rstudioapi_0.17.1 
[57] vroom_1.6.5        jsonlite_1.8.9     R6_2.5.1           fs_1.6.5          
[61] systemfonts_1.1.0 

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2026_04.qmd.

For the full repository, click here.

10. References

Expand for References

Primary Data (Makeover Monday):

1. Makeover Monday 2026 Week 4: Most Posted US Jobs by State

2. Original Article: Job titles in highest-demand, state by state

   • Source: Lightcast Job Posting Analytics
   • Coverage: Top 3 job postings by state (Oct 2024 - Oct 2025)

Enhancement Data:

3. Employment & Wage Data: BLS Occupational Employment and Wage Statistics (OEWS)
   • Release: May 2024 State Estimates
   • Variables: Employment counts, mean annual wages by occupation × state
   • Citation: U.S. Bureau of Labor Statistics. (2025). Occupational Employment and Wage Statistics, May 2024.
4. Population Data: Census Bureau Population Estimates
   • Year: 2023 State Population Estimates
   • Citation: U.S. Census Bureau. (2023). Annual Estimates of the Resident Population for the United States, Regions, States, District of Columbia, and Puerto Rico: April 1, 2020 to July 1, 2023 (NST-EST2023-POP).

SOC Codes Used (BLS OEWS):

• 29-1141: Registered Nurses
• 53-3032: Heavy and Tractor-Trailer Truck Drivers
• 41-2031: Retail Salespersons
• 15-1252: Software Developers
• 43-4051: Customer Service Representatives
• 29-2061: Licensed Practical and Licensed Vocational Nurses

11. Custom Functions Documentation

📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils